A novel hybrid energy management strategy of a diesel-electric hybrid ship based on dynamic programing and model predictive control

The energy-saving characteristics of diesel-electric series hybrid ships largely depend on their energy management strategy. In this paper, a strategy that combines dynamic programing and model predictive control (DP-MPC) is proposed to solve the energy management problems of diesel-electric hybrid ships. The DP-MPC strategy has considered some typical working conditions of a ship, and the corresponding influence of white noise disturbance on the control strategy was studied. The simulation results show that the DP-MPC strategy has an excellent anti-interference capability. The control performance of the DP-MPC strategy is then further analyzed and compared with the rule-based logic threshold control strategy. The simulation results show that the proposed DP-MPC strategy can save 2.5% of the fuel consumption and has a better anti-interference capability than the rule-based control strategy.

Research on Peak Load Shifting Operation Strategy of Small-Scale Mobile Energy Storage Device in Distribution Network

This paper studied two methods of using energy storage (ES) to achieve peak load shifting by charging and discharging. In different distribution network transformer power supply areas, the time of peak and valley of load is not identical. The small-scale mobile ES device was commanded to deliver energy at the peak of the power supply area to reduce the supply pressure of the power system by central control units (CCU), and while the load is at the valley, the ES charges and absorbs power to enhance the utilization rate of energy in the system. Currently, the control methods of peak load shifting for ES device (Constant Power Control and Threshold Control) were studied, and the operation results in power system were analyzed with their involvement, which provided the substantial supports for CCU to dispatch. Ultimately, the results showed the methods proposed were feasible and effective by simulation.

Sliding Dynamics and Bifurcations of a Filippov System with Nonlinear Threshold Control

Considering the effectiveness of introducing the change rate of viral loads into the threshold setting policy for triggering interventions, we propose an immune-virus Filippov system with a nonlinear threshold. By developing new analytical and numerical methods, we systematically studied the rich dynamical behaviors and bifurcations of the proposed system, including the existence of three sliding segments and three pseudo-equilibria, boundary-center bifurcation, boundary-saddle bifurcation, pseudo-saddle-node bifurcation and tangency bifurcation. We further showed that the proposed system can exhibit virous structures in the coexistence of multiple steady states. Phenomena include bistability of two pseudo-equilibria, tristability and multiplestability of two pseudo-equilibria with regular equilibria or touching cycles. The modeling methods, as well as the analytical and numerical methods, can be widely applied to many other fields.

New Control Scheme for Solar Power Systems under Varying Solar Radiation and Partial Shading Conditions

The traditional perturbation and observation (P&O) maximum power point tracking (MPPT) algorithm of a structure is simple and low-cost. However, the P&O algorithm is prone to divergence under solar radiation when the latter varies rapidly and the P&O algorithm cannot track the maximum power point (MPP) under partial shading conditions (PSCs). This study proposes an algorithm from the P&O algorithm combined with the solar radiation value detection scheme, where the solar radiation value detection is based on the solar photovoltaic (SPV) module equivalent conductance threshold control (CTC). While the proposed algorithm can immediately judge solar radiation, it also has suitable control strategies to achieve the high efficiency of MPPT especially for the rapid change in solar radiation and PSCs. In the actual test of the proposed algorithm and the P&O algorithm, the MPPT efficiency of the proposed algorithm could reach 99% under solar radiation, which varies rapidly, and under PSCs. However, in the P&O algorithm, the MPPT efficiency was 96% under solar radiation, which varies rapidly, while the MPPT efficiency was only 80% under PSCs. Furthermore, in verifying the experimental results, the proposed algorithm’s performance was higher than the P&O algorithm.

DCSK performance analysis of a chaos-based communication using a newly designed chaotic system

In this paper, a new chaotic system has been introduced and the fundamental properties of the system were investigated and presented using a bifurcation diagram, max Lyapunov exponent (LE) and phase portraits. The synchronization of the drive and response system has been done using the threshold control parameter. Later the differential chaos shift keying (DCSK) modulation scheme has been carried out for the system as it is the most efficient modulation scheme. The demodulator detects the data without the use of chaotic signal phase recovery, as it uses the non-coherent detection technique. The results were compared with other modulation schemes using the bit error rate (BER) graph. It reveals that the proposed chaos-based system could be used for secure communication. The system has been implemented using the MATLAB Simulink technique.

Intelligent Calibration of Static FEA Computations Based on Terrestrial Laser Scanning Reference

The demand for efficient and accurate finite element analysis (FEA) is becoming more prevalent with the increase in advanced calibration technologies and sensor-based monitoring methods. The current research explores a deep learning-based methodology to calibrate FEA results. The utilization of monitoring reference results from measurements, e.g., terrestrial laser scanning, can help to capture the actual features in the static loading process. We learn the deviation sequence results between the standard FEA computations with the simplified geometry and refined reference values by the long short-term memory method. The complex changing principles in different deviations are trained and captured effectively in the training process of deep learning. Hence, we generate the FEA sequence results corresponding to next adjacent loading steps. The final FEA computations are calibrated by the threshold control. The calibration reduces the mean square errors of the FEA future sequence results significantly. This strengthens the calibration depth. Consequently, the calibration of FEA computations with deep learning can play a helpful role in the prediction and monitoring problems regarding the future structural behaviors.